Super-resolution (SR) techniques have recently been proposed to upscale the outputs of neural radiance fields (NeRF) and generate high-quality images with enhanced inference speeds. However, existing NeRF+SR methods increase training overhead by using extra input features, loss functions, and/or expensive training procedures such as knowledge distillation. In this paper, we aim to leverage SR for efficiency gains without costly training or architectural changes. Specifically, we build a simple NeRF+SR pipeline that directly combines existing modules, and we propose a lightweight augmentation technique, random patch sampling, for training. Compared to existing NeRF+SR methods, our pipeline mitigates the SR computing overhead and can be trained up to 23x faster, making it feasible to run on consumer devices such as the Apple MacBook. Experiments show our pipeline can upscale NeRF outputs by 2-4x while maintaining high quality, increasing inference speeds by up to 18x on an NVIDIA V100 GPU and 12.8x on an M1 Pro chip. We conclude that SR can be a simple but effective technique for improving the efficiency of NeRF models for consumer devices.

翻译：超分辨率(SR)技术近期被提出用于提升神经辐射场(NeRF)的输出分辨率，在增强推理速度的同时生成高质量图像。然而，现有NeRF+SR方法通过引入额外输入特征、损失函数和/或知识蒸馏等昂贵的训练流程，增加了训练开销。本文旨在利用SR技术实现效率提升，同时避免昂贵的训练或架构改动。具体而言，我们构建了一个直接组合现有模块的简单NeRF+SR流水线，并提出了一种轻量级训练增强技术——随机块采样。与现有NeRF+SR方法相比，我们的流水线降低了SR计算开销，训练速度可提升至23倍，使其能够在Apple MacBook等消费设备上运行。实验表明，我们的流水线能将NeRF输出分辨率提升2-4倍，同时保持高质量，在NVIDIA V100 GPU上推理速度提升至18倍，在M1 Pro芯片上提升至12.8倍。我们得出结论：SR是提升消费设备上NeRF模型效率的一种简单而有效的技术。